Active-Active Node Redundancy vs Active-Passive Failover

Simultaneous operation: All nodes process requests, eliminating failover delay. This matters for high-frequency trading (HFT) protocols and real-time settlement layers where even sub-second downtime causes arbitrage losses or failed transactions.

Load distribution: Incoming traffic is shared across all nodes, increasing total system capacity. This matters for public RPC providers (like Infura, Alchemy) and high-TPS L2 sequencers that must handle thousands of concurrent user requests without throttling.

Single source of truth: Only the active primary node writes to the database, avoiding consensus conflicts. This matters for oracle networks (like Chainlink) and cross-chain bridges where transaction ordering and finality must be unambiguous to prevent double-spend or incorrect price feeds.

Reduced resource consumption: Passive standby nodes require minimal compute until failover. This matters for cost-sensitive enterprise validators and private consortium chains where running multiple full-capacity nodes for the same shard or service is economically prohibitive.

Parallel Processing: All nodes handle live traffic, enabling linear scaling of read/write capacity. This is critical for high-TPS applications like DEX aggregators (e.g., 1inch) or gaming protocols that require sub-second finality.

• Use Case Fit: Real-time data feeds, high-frequency DeFi, and public RPC endpoints.

Seamless Fault Tolerance: User sessions and transactions automatically reroute to healthy nodes with no service interruption. This is essential for protocols like Aave or Compound where a few seconds of downtime can trigger liquidations.

• Trade-off: Requires sophisticated load balancers (e.g., HAProxy, AWS ALB) and session-aware routing.

Simpler State Management: Only one primary node handles writes, eliminating consensus and data synchronization conflicts between active nodes. This simplifies deployment for internal indexers or oracles (e.g., Chainlink) where consistency is paramount over raw speed.

• Use Case Fit: Internal APIs, backup data pipelines, and consensus-critical services.

Idle Resource Efficiency: Passive (standby) nodes can run on smaller, cheaper instances until failover. This optimizes budget for projects like NFT marketplaces with spiky, predictable traffic patterns, where over-provisioning is wasteful.

• Trade-off: Failover event incurs a brief service interruption (typically 30-120 seconds) for state sync.

Resource Intensive: Requires full-scale, identical nodes running concurrently, doubling or tripling cloud compute costs. Synchronization overhead for tools like The Graph's indexers or Etherscan-like explorers can become a bottleneck.

• Decision Trigger: Justify only if your SLA requires >99.99% uptime or you serve >10K RPS.

Single Point of Load: All traffic hits the primary node, creating a scaling ceiling. During failover, the promotion lag causes service disruption—unacceptable for algorithmic trading bots or perpetual swap platforms like dYdX.

• Key Metric: Recovery Time Objective (RTO) is never zero.

Simultaneous load distribution: All nodes process requests, maximizing resource utilization. This is critical for high-TPS applications like DEX aggregators (e.g., 1inch) or gaming protocols that require sub-second finality. It eliminates the idle resource tax of passive standby nodes.

Seamless user experience: Traffic is instantly rerouted to healthy nodes with no service interruption. This is non-negotiable for financial protocols (e.g., Aave, Compound) where even a few seconds of downtime can lead to liquidations or arbitrage losses. The failover is handled at the load balancer level.

Higher operational overhead: Requires sophisticated state synchronization (e.g., for RPC endpoints) and consensus on data consistency. Tools like Consul or Kubernetes with service meshes are often needed. This increases DevOps complexity and cloud compute costs by 40-60% compared to a passive setup.

Easier to implement and maintain: A single active node handles all traffic, with a passive replica on standby. This is ideal for internal indexers, oracles (Chainlink), or governance backends where extreme throughput isn't required. Infrastructure costs are predictable and significantly lower.

Deterministic recovery process: Failover mechanisms (e.g., AWS Route 53 health checks, floating IPs) are well-understood and reliable. This suits applications with tolerable downtime SLAs (e.g., 99.5%), such as analytics dashboards (Dune, The Graph) or batch-processing jobs.

Standby resource waste: The passive node incurs costs while providing zero production value. Failover incurs downtime: Even with fast health checks, there is a 30-120 second service interruption during switchover. This is unacceptable for real-time trading or wallet RPC services.